PLINK cheatsheet
In tidypopgen: Tidy Population Genetics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(tidypopgen)
# Create gen_tibble for examples
bed_path <- system.file("extdata/pop_a.bed", package = "tidypopgen")
bigsnp_path <- bigsnpr::snp_readBed(bed_path, backingfile = tempfile())
data <- gen_tibble(bigsnp_path, quiet = TRUE)

The following flags and corresponding tidypopgen functions are based on plink version 1.9.

File management and reading data:

| PLINK 1.9 | tidypopgen | |----------------------------------|----------------------------------------------------------| | --make-bed --out | gt_as_plink(data, file = my_file, type = "bed") | | --recode | gt_as_plink(data, file = my_file, type = "ped") | | --recode vcf | gt_as_vcf(data, file = my_file) | | --allele1234 and --alleleACGT | See gen_tibble() parameter 'valid_alleles' |

PLINK flags --update-alleles, --allele1234, and --alleleACGT, all alter the coding of alleles. In tidypopgen, valid alleles are supplied when reading in a gen_tibble.

Quality control:

| PLINK | tidypopgen | |--------------------|---------------------------------| | --maf | data %>% loci_maf() | | --geno | data %>% loci_missingness() | | --hwe | data %>% loci_hwe() | | --freq --a2-allele | data %>% loci_alt_freq() | | --mind | data %>% indiv_missingness() | | --het | data %>% indiv_het_obs() |

To filter out variants in tidypopgen, in a similar way to PLINK flags such as --extract or --autosome, it is necessary to use the gen_tibble with select_loci_if(). For example:

data %>% select_loci_if(loci_chromosomes(genotypes) %in% c(1:22))

will select autosomal loci in the same way as --autosome. Or alternatively:

my_snps <- c("rs4477212", "rs3094315", "rs3131972", "rs12124819", "rs11240777")

data %>%
  select_loci_if(loci_names(genotypes) %in% my_snps) %>%
  show_loci()

will select loci from a previously defined set in the same way as --extract.

Similarly, to filter out individuals, as might be performed with --keep in PLINK, requires using filter:

my_individuals <- c("GRC14300079", "GRC14300142", "GRC14300159")

data %>% filter(id %in% my_individuals)

Handling linkage

Linkage disequilibrium is managed through clumping in tidypopgen with loci_ld_clump().

This option is similar to the --indep-pairwise flag in PLINK, but results in a more even distribution of loci when compared to LD pruning.

To explore why clumping is preferable to pruning, see https://privefl.github.io/bigsnpr/articles/pruning-vs-clumping.html

Quality control for relatedness (KING)

| KING | tidypopgen | |-------------|-----------------------------| | --kinship | pairwise_king() | | --distance | pairwise_ibs() | | --unrelated | filter_high_relatedness() |

pairwise_king() implements the KING-robust estimator of kinship, equivalent to --kinship in KING. To remove related individuals, the user can pass the kinship matrix of pairwise_king() and a relatedness threshold (a numeric KING kinship coefficient) to filter_high_relatedness(), which will return the largest possible set of individuals with no relationships above the threshold.

pairwise_king() also forms part of qc_report_indiv() through the parameter kings_threshold. To remove related individuals in qc_report_indiv(), the user can pass either a relatedness threshold, or a string of either "first" or "second" to remove any first degree or second degree relationships from the dataset. This second option is similar to using --unrelated --degree 1 or --unrelated --degree 2 in KING. qc_report_indiv() returns a dataframe including columns 'id' and 'to_keep', showing the ID of individuals and a logical column of whether the individual should be removed to retain the largest possible set of individuals with no relationships above the threshold.

Merging datasets:

| PLINK | tidypopgen | |-------------|---------------------------------------| | --bmerge | rbind() | | --flip-scan | rbind_dry_run() | | --flip | use 'flip_strand = TRUE' in rbind() |

In PLINK, data merging can fail due to strand inconsistencies that are not addressed prior to merging. PLINK documentation suggests to users to try a 'trial flip' of data to address this, and then to 'unflip' any errors that remain. In tidypopgen, when data are merged with rbind, strand inconsistencies are identified and automatically flipped, avoiding multiple rounds of flipping before merging.

PLINK does allow users to identify inconsistencies prior to merging with --flip-scan, and this functionality is included in the tidypopgen rbind_dry_run(). rbind_dry_run() reports the numeric overlap of datasets, alongside the number of SNPs to 'flip' in the new target dataset, as well as the number of ambiguous SNPs.

Data are only merged one set at a time, there is no equivalent to --merge-list.

Analysis:

| PLINK | tidypopgen | |-----------|----------------------------------------| | --pca | See gt_pca() for pca options | | --fst | pairwise_pop_fst() with group_by() | | --homozyg | windows_indiv_roh() |